library(Seurat)
library(dplyr)
library(Matrix)
Attaching package: ‘Matrix’
The following object is masked from ‘package:reshape’:
expand
library(ggplot2)
Read in the 4 sorted Populations separately processed and annotated
Check each object for main cell type labels
table(Neurons1$Cell_Types)
dim(Neurons1) # total cells 1723
table(Neurons2$Cell_Types)
dim(Neurons2) # total cells 8884
table(Astro$Cell_Type)
dim(Astro) # 20000
table(RG$Cell_Types)
dim(RG)
Fix each separate populations - Add FACS sort name - Down sample - check proportions are maintained - rename Cell_Type in Astro to be the same name for ‘Cell_Types’
# cell type variables weren't named the same and the project ID are not the population names
# I'll further down sample Glia1 (LaunchSample3)
# change orig.ident names
Idents(Neurons2) <- 'orig.ident'
Neurons2$orig.ident <- 'Neurons2'
Idents(Astro) <- 'orig.ident'
Astro$orig.ident <- 'Astrocytes'
Idents(RG) <- 'orig.ident'
RG$orig.ident <- 'RadialGlia'
# rename metadata
Idents(Astro) <- 'Cell_Type'
Astro$Cell_Types <- Idents(Astro)
table(Astro$Cell_Types)
Astro$Cell_Type <- NULL
# down sample
Idents(Astro) <- 'orig.ident'
Astro.sub <- subset(Astro, downsample = 9000)
table(Astro.sub$Cell_Types)
# good proportions are maintained
# save downsampled object
all.pops <- merge(Neurons1, y = c(Neurons2, Astro.sub, RG), project = "PhenoID")
Try to down sample to get closer proportions of starting populations
# down sample
Idents(Astro) <- 'orig.ident'
Astro.sub <- subset(Astro, downsample = 3000)
table(Astro$Cell_Types)
table(Astro.sub$Cell_Types)
# down sample
Idents(Neurons2) <- 'orig.ident'
Neurons2.sub <- subset(Neurons2, downsample = 2000)
dim(Neurons2)
table(Neurons2$Cell_Types)
table(Neurons2.sub$Cell_Types)
# down sample
Idents(RG) <- 'orig.ident'
RG.sub <- subset(RG, downsample = 2000)
dim(RG)
table(RG$Cell_Types)
table(RG.sub$Cell_Types)
all.pops <- merge(Neurons1, y = c(Neurons2.sub, Astro.sub, RG.sub), project = "PhenoID")
table(all.pops$orig.ident)
Check merge
table(all.pops$orig.ident)
table(all.pops$Cell_Types)
# I might want to down sample all the other populations accept Neurons1
# they cluster fairly well
# save the merged object
pathway <- "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/"
saveRDS(all.pops, paste(pathway,"All4popssub07102022.RDS"))
#seu.q <- readRDS(paste(pathway,"All4pops07102022.RDS"))
#seu.q <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/All4popssub07102022.RDS")
Normalize and cluster
# standard workflow
# repeated same for all.pops and all.pops sub
seu <- NormalizeData(all.pops, normalization.method = "LogNormalize", scale.factor = 10000)
seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2500)
seu <- ScaleData(seu)
seu <- RunPCA(seu)
ElbowPlot(seu, ndims = 30)
# best choice PC = 15
# didn't save the objects need to repeat
seu.q <- NormalizeData(seu.q, normalization.method = "LogNormalize", scale.factor = 10000)
seu.q <- FindVariableFeatures(seu.q, selection.method = "vst", nfeatures = 2500)
seu.q <- ScaleData(seu.q)
seu.q <- RunPCA(seu.q)
ElbowPlot(seu.q, ndims = 30)
Find clusters
seu <- RunUMAP(seu, reduction = "pca", n.neighbors = 25, dims = 1:20)
DimPlot(seu, reduction = "umap")
seu.q <- FindNeighbors(seu, dims = 1:20, k.param = 25)
seu.q <- FindClusters(seu.q, resolution = c(0,0.2,0.6,1,1.2, 1.5, 1.8))
# clear out other clustering to be able to use clustree
library(clustree)
#columns.to.remove <- c("RNA_snn_res.0.05", "RNA_snn_res.0.1","RNA_snn_res.0.25","RNA_snn_res.0.5",
# "RNA_snn_res.0.4","RNA_snn_res.0.8")
#for(i in columns.to.remove) {
# seu.q[[i]] <- NULL
#}
#DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.05')
#DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.1')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.2', label = TRUE)
#DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.4')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.6',label = TRUE)
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.1', label = TRUE)
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.1.2', label = TRUE)
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.1.8', label = TRUE)
DimPlot(seu.q, group.by = 'orig.ident', label = TRUE)
#VlnPlot(seu.q, features = 'nFeature_RNA', group.by = 'orig.ident')
DimPlot(seu.q, group.by = 'Cell_Types', label = TRUE)
DimPlot(seu.q, group.by = 'Cell_Types', split.by = 'orig.ident')
DimPlot(seu.q, group.by = 'RNA_snn_res.1.8', split.by = 'orig.ident')
# repeat when object wasn't saved
seu.q <- RunUMAP(seu.q, reduction = "pca", n.neighbors = 25, dims = 1:20)
DimPlot(seu.q, reduction = "umap")
seu.q <- FindNeighbors(seu.q, dims = 1:20, k.param = 25)
seu.q <- FindClusters(seu.q, resolution = c(0,0.2,0.6,1,1.2, 1.8))
# clear out other clustering to be able to use clustree
library(clustree)
columns.to.remove <- c("RNA_snn_res.0.05", "RNA_snn_res.0.1","RNA_snn_res.0.25","RNA_snn_res.0.5",
"RNA_snn_res.0.4","RNA_snn_res.0.8")
for(i in columns.to.remove) {
seu.q[[i]] <- NULL
}
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.1.8', label = TRUE)
DimPlot(seu.q, group.by = 'orig.ident', label = TRUE)
DimPlot(seu.q, group.by = 'Cell_Types', label = TRUE)
DimPlot(seu.q, group.by = 'Cell_Types', split.by = 'orig.ident')
DimPlot(seu.q, group.by = 'RNA_snn_res.1.8', split.by = 'orig.ident')
clustree(seu.q)
Number FACS populations in each cluster Number of Cell Types in each cluster
# FACS populations per cluster
table(seu.q$RNA_snn_res.1.8, seu.q$orig.ident)
# Cell Types per cluster
table(seu.q$RNA_snn_res.1.8, seu.q$Cell_Types)
Re annotate these clusters with subgroups
Idents(seu.q) <- 'RNA_snn_res.1.8'
# from k.param 49
cluster.ids <- c("Astro1","Neurons1","RG1","Astro2",
"RG2","Neurons2","Neurons3","Astro3",
"Neurons3","Astro4","Astro5","Neurons4",
"RG3","RG4","Neurons5","Neurons6",
"Endothelial","Neurons7")
# from k.param 249
cluster.ids <- c("RG1","Neurons1","Astro1","Astro2","Neurons2",
"Astro-Mix","Neurons-RG","RG2",
"Neurons3",
"Endothelial")
# k.param 125
cluster.ids <- c("Astro1","Neurons1","Astro2","Neurons1",
"RG1-Neurons","Neurons2","Astro3-Neurons-RG","RG1",
"Neurons3","Neurons4","RG2",
"Endothelial","Mix")
# k.param 25
cluster.ids <- c("Astro1","Neurons1","RG-Neurons1","Astro2","Neurons2",
"Astro-RG","Astro3","Neurons2","RG2","Neurons3",
"Astro3","Neurons4","RG3","Neurons4","Neurons5",
"Mix","RG4","Endothelial","Neurons6","RG-Neurons2",
"Neurons7","Glia","RG5","Mix","Mix"
)
names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$Cluster.ids <- Idents(seu.q)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'Cluster.ids', repel = TRUE)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'Cluster.ids',split.by = 'orig.ident')
# some cluster are problematic
# cluster 3 has 650 RG and 180 Neurons
Idents(seu.q) <- 'RNA_snn_res.1.8'
cluster.ids <- c("Astro","Neurons","RG","Astro",
"RG","Neurons","Neurons","Astro",
"Neurons","Astro","Astro","Neurons",
"RG","RG","Neurons","Neurons",
"Endothelial","Neurons")
cluster.ids <- c("RG","Neurons","Astro","Astro","Neurons",
"Astro-Mix","Neurons-RG","RG",
"Neurons",
"Endothelial")
# k.param 125
cluster.ids <- c("Astro","Neurons","Astro","Neurons",
"RG1-Neurons","Neurons","Astro3-Neurons-RG","RG",
"Neurons","Neurons","RG",
"Endothelial","Mix")
# K 25
cluster.ids <- c("Astro","Neurons","RG-Neur","Astro","Neurons",
"Astro-RG","Astro","Neurons","RG","Neurons",
"Astro","Neurons","RG","Neurons","Neurons",
"Mix","RG","Endothelial","Neurons","RG-Neur",
"Neurons","Glia","RG","Mix","Mix"
)
names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$Level1 <- Idents(seu.q)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'Level1', repel = TRUE)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'Level1',split.by = 'orig.ident')
Make a table of the proportion of cell types and a plot
cell.order
Error: object 'cell.order' not found
Make a proprotion table
library(stringr)
library(reshape)
# for original cell types
df.r <- reshape(df, idvar = "Var2", timevar = "Var1", direction = "wide")
dat1 <- df.r
dat1[] <- lapply(dat1[], function(x){
# Check if the column is numeric
if (is.numeric(x)){
return(x/sum(x)*100)
} else{
return(x)
}
})
dat1
write.csv(dat1, paste(output_path,"proportionofCelltypesin4pops.csv"))
### for new main cell types
df.r <- reshape(df.3, idvar = "Var2", timevar = "Var1", direction = "wide")
dat3 <- df.r
dat3[] <- lapply(dat3[], function(x){
# Check if the column is numeric
if (is.numeric(x)){
return(x/sum(x)*100)
} else{
return(x)
}
})
dat3
### with the subtypes of cells
df.r <- reshape(df.2, idvar = "Var2", timevar = "Var1", direction = "wide")
dat2 <- df.r
dat2[] <- lapply(dat2[], function(x){
# Check if the column is numeric
if (is.numeric(x)){
return(x/sum(x)*100)
} else{
return(x)
}
})
dat2
Process Kamath
seu <- NormalizeData(seu.r, normalization.method = "LogNormalize", scale.factor = 10000)
seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)
seu <- ScaleData(seu)
seu <- RunPCA(seu)
saveRDS(seu,"/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/KamathTotal_downsample.RDS")
Re annotate the new clustered object
Get predictions: Use Kamath all cells down sampled (doesn’t contain Radial Glia) Developing Forebrain Organoids AIW002 120 days
# Kamath midbrain Postmortem sn
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/KamathTotal_downsample.RDS")
#Idents(seu.r) <- "Cell_Type"
#seu.r <- subset(seu.r, idents = c("astro","da","nonda","endo"))
Idents(seu.r) <- "Cell_Subtype"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$Cell_Subtype, k.weight = 50)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "mb.pred")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")
table(seu.q$mb.pred)
seu.q$mb.pred.thresh <- ifelse(seu.q$prediction.score.max > 0.7, seu.q$mb.pred, "none")
DimPlot(seu.q, group.by = 'mb.pred.thresh')
table(seu.q$mb.pred.thresh)
#***** subtype predictions don't work
#but when I had the separate subytes they did work on neurons and astro separately. It could be there just aren't enough reference cells - I only have 1000 each main group.
# I'm going to try with the main cell types groups instead
Idents(seu.r) <- "Cell_Type"
table(seu.r$Cell_Type)
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$Cell_Type)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "mb.pred")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")
table(seu.q$mb.pred)
seu.q$mb.pred.thresh <- ifelse(seu.q$prediction.score.max > 0.7, seu.q$mb.pred, "none")
DimPlot(seu.q, group.by = 'mb.pred.thresh', label = TRUE)
table(seu.q$mb.pred.thresh)
# create the prediction top 2 cell types per cluster dataframe
# still useless
# best to use the subtype predictions from the separate objects after subsetting
Try the Bhaduri whole brain dataset
# midbrain and striatum
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Bhaduri_midbrain_striatum.RDS")
Idents(seu.r) <- "cell_cluster"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$cell_cluster)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "mbs.pred")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")
table(seu.q$mbs.pred)
seu.q$mbs.pred.thresh <- ifelse(seu.q$prediction.score.max > 0.7, seu.q$mbs.pred, "none")
DimPlot(seu.q, group.by = 'mbs.pred.thresh', label = TRUE)
table(seu.q$mb.pred.thresh)
# read in the reference dataset
# whole brain Bhaduri down sampled
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Bhaduri_downsample.RDS")
Idents(seu.r) <- "cell_cluster"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$cell_cluster)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "brain.pred")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")
table(seu.q$brain.pred)
seu.q$brain.pred.thresh <- ifelse(seu.q$prediction.score.max > 0.7, seu.q$brain.pred, "none")
DimPlot(seu.q, group.by = 'brain.pred.thresh', label = TRUE)
DimPlot(seu.q, group.by = "brain.pred")
table(seu.q$brain.pred.thresh)
Label transfer with the developing forbrain
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Karolinski_DevForebrain_downsample_Level1.RDS")
colnames(seu.r@meta.data)
DefaultAssay(seu.r) <- 'RNA'
# clusters has subgroups
# levels are main cell groups
Idents(seu.r) <- "Level1"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$Level1)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "dfb.pred")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")
table(seu.q$dfb.pred)
seu.q$dfb.pred.thresh <- ifelse(seu.q$prediction.score.max > 0.85, seu.q$dfb.pred, "none")
table(seu.q$dfb.pred.thresh)
DimPlot(seu.q, group.by = 'dfb.pred.thresh', label = FALSE)
DimPlot(seu.q, group.by = "dfb.pred")
DimPlot(seu.q, group.by = 'orig.ident', split.by = 'dfb.pred.thresh')
DimPlot(seu.q, split.by = 'orig.ident', group.by = 'dfb.pred.thresh')
AIW002 120 days predictions
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio120days/MOintegratedClusterK123res0.8.names_nov16_2021")
### maybe the knockouts are causing a problem
Idents(seu.r) <- 'orig.ident'
seu.r <- subset(seu.r, idents = )
Idents(seu.r) <- "res08names"
DefaultAssay(seu.r) <- "RNA"
anchors <- FindTransferAnchors(reference = seu.r ,query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$res08names)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "hmo.pred")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")
table(seu.q$hmo.pred)
seu.q$hmo.pred.thresh <- ifelse(seu.q$prediction.score.max > 0.85, seu.q$hmo.pred, "none")
table(seu.q$hmo.pred.thresh)
DimPlot(seu.q, group.by = 'hmo.pred.thresh', label = FALSE)
DimPlot(seu.q, group.by = "hmo.pred")
DimPlot(seu.q, group.by = 'orig.ident', split.by = 'hmo.pred.thresh')
DimPlot(seu.q, split.by = 'orig.ident', group.by = 'hmo.pred.thresh')
Make a summary table of predictions
# AIW002 120 days predictions - take the thresholded options
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.1.8, seu.q$hmo.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
Try the 60 days organoids AIW
seu.r <-readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio60days/AWI002ParkinKOPinkKO60days_labels_14052022.rds")
Idents(seu.r) <- "cluster.ids"
DefaultAssay(seu.r) <- "RNA"
anchors <- FindTransferAnchors(reference = seu.r ,query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$cluster.ids)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "hmo60.pred")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")
table(seu.q$hmo60.pred)
seu.q$hmo60.pred.thresh <- ifelse(seu.q$prediction.score.max > 0.85, seu.q$hmo60.pred, "none")
table(seu.q$hmo60.pred.thresh)
DimPlot(seu.q, group.by = 'hmo60.pred.thresh', label = FALSE)
DimPlot(seu.q, group.by = "hmo60.pred")
DimPlot(seu.q, group.by = 'orig.ident', split.by = 'hmo60.pred.thresh')
DimPlot(seu.q, split.by = 'orig.ident', group.by = 'hmo60.pred.thresh')
Make predictions from 60 days
# AIW002 120 days predictions - take the thresholded options
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.1.8, seu.q$hmo60.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
Look at the expression of gene lists
Idents(seu.q) <- 'RNA_snn_res.1.8'
feature_list = c("MKI67","SOX2","POU5F1","DLX2","PAX6","SOX9","HES1","NES","RBFOX3","MAP2","NCAM1","CD24","GRIA2","GRIN2B","GABBR1","GAD1","GAD2","GABRA1","GABRB2","TH","ALDH1A1","LMX1B","NR4A2","CORIN","CALB1","KCNJ6","CXCR4","ITGA6","SLC1A3","CD44","AQP4","S100B", "PDGFRA","OLIG2","MBP","CLDN11","VIM","VCAM1")
#DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = feature_list) +RotatedAxis()
PD_poulin = c("TH","SLC6A3","SLC18A2","SOX6","NDNF","SNCG","ALDH1A1","CALB1","TACR2","SLC17A6","SLC32A1","OTX2","GRP","LPL","CCK","VIP")
#DoHeatmap(seu.q, features = PD_poulin, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = PD_poulin)+RotatedAxis()
ealryNeur = c("DCX","NEUROD1","TBR1")
proliferation = c("PCNA","MKI67")
neuralstem = c("SOX2","NES","PAX6","MASH1")
feature_list <- c("DCX","NEUROD1","TBR1","PCNA","MKI67","SOX2","NES","PAX6","MASH1")
#DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = feature_list)+RotatedAxis()
# no proliferation marker expression PCNA or MKI67
# cluster 4 DA neurons - shows early neuron marker and low PAX 4
# cluster 3 has higher SOX2 - neuroblast marker / NPC marker
mat_neuron = c("RBFOX3","SYP","DLG45","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6")
# NeuN is FOX3 - RBFOX3
# PSD95 also SP-90 or DLG4
# VGLUT2 is SLC17A6
#DoHeatmap(seu.q, features = mat_neuron, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
# cluster 4 also show mature neuron markers
DotPlot(seu.q, features = mat_neuron)+RotatedAxis()
# excitatory neuron markers
ex = c("GRIA2","GRIA1","GRIA4","GRIN1","GRIN2B","GRIN2A","GRIN3A","GRIN3","GRIP1","CAMK2A")
#DoHeatmap(seu.q, features = ex, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = ex)+RotatedAxis()
# inhibitory neuron markers
inh = c("GAD1","GAD2", "GAT1","PVALB","GABR2","GABR1","GBRR1","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
#DoHeatmap(seu.q, features = inh, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = inh)+RotatedAxis()
# cluster 4 is more excitatory than inhbitory but neither marker set has much expression
### glia markers
microglia = c("PTPRC","AIF1","ADGRE1") # ADGRE1 is a microglia marker F4/80, CD45 is PTPRC, gene name IBA1 is AIF1
astolgNPCpromicro = c("GFAP","S100B","SLC1A2","MBP","SOX10","SPP1","DCX","NEUROD1","TBR1","PCNA","MKI67","PTPRC","AIF1","ADGRE1")
# note GLT1 is EAAT2 which is SLC1A2 glutatmate transporter
# epithelial
epi = c("HES1","HES5","SOX2","SOX10","NES","CDH1","NOTCH1") # e-cadherin is CDH1
#DoHeatmap(seu.q, features = astolgNPCpromicro, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = astolgNPCpromicro)+RotatedAxis()
# cluster 4 is more excitatory than inhbitory but neither marker set has much expression
#DoHeatmap(seu.q, features = epi, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = epi)+RotatedAxis()
# also add Radial glia marker overlap with Glia and Neurons
features <- c("PTPRC","AIF1","ADGRE1", "VIM", "TNC","PTPRZ1","FAM107A","HOPX","LIFR",
"ITGB5","IL6ST")
#DoHeatmap(seu.q, features = features, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = features)+RotatedAxis()
Rename cell types
Idents(seu.q) <- 'RNA_snn_res.1.8'
cluster.ids <- c("Astro-RG-Pre","Neurons1-im","RG1-NPC","Astro1","Neurons2-im",
"Astro-NPC","Astro","Neurons3","RG2","Neurons4",
"Astro3","Neurons5","RG3-NPC","Neurons6-DA","Neurons7-NPC",
"Mix-RG4","RG5","Endothelial-NPC","Neurons8-inh-max","Neurons9-DA",
"Neurons10-DA","RG6","RG7-epi","NPC-Astro","RG-OPC"
)
names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$Level2 <- Idents(seu.q)
DimPlot(seu.q, group.by = 'Level2', label = TRUE)
DimPlot(seu.q, group.by = 'Level2', label = TRUE, split.by = 'orig.ident')
DimPlot(seu.q, group.by = 'orig.ident')
# name the major groups together and see how it matches up with space
cluster.ids <- c("Astro1","Neurons1","RG1","Astro2","Neurons2",
"Astro3","Astro4","Neurons3","RG2","Neurons4",
"Astro5","Neurons5","RG3-NPC","DANeurons1","Neurons6",
"Mix","RG4","Endothelial-NPC","Neurons8-inh-max","DANeurons2",
"DANeurons3","RG5","RG6-epi","RG7","RG-OPC"
)
names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$Level3 <- Idents(seu.q)
DimPlot(seu.q, group.by = 'Level3', label = TRUE, repel = TRUE)
DimPlot(seu.q, group.by = 'Level3', label = TRUE, split.by = 'orig.ident')
DimPlot(seu.q, group.by = 'orig.ident')
Idents(seu.q) <- 'RNA_snn_res.1.8'
cluster.ids <- c("Astro","Neurons","RG","Astro","Neurons",
"Astro","Astro","Neurons","RG","Neurons",
"Astro","Neurons","RG","DANeurons","Neurons",
"Mix","RG","Endothelial","Neurons","DANeurons",
"DANeurons","RG","RG","RG","RG"
)
names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$Levels <- Idents(seu.q)
DimPlot(seu.q, group.by = 'Levels', label = TRUE, repel = TRUE)
DimPlot(seu.q, group.by = 'Levels', label = TRUE, split.by = 'orig.ident')
DimPlot(seu.q, group.by = 'orig.ident')
See how the labels fit with sorted cell types
t.lables <- as.data.frame(table(seu.q$orig.ident, seu.q$Level3))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
#Not a bad breakdown mix
t.lables <- as.data.frame(table(seu.q$orig.ident, seu.q$Levels))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
Make the cell type proportion plots - make the input colours should be different than cell type colours Cell type Use colours from other figures if possible: Astrocytes = orange Orange Radial Glia = Pink Neurons = Purple Endothelial bright blue
I’ll make a UMAP with the input colours - match to 2D -
Find Cluster markers with all clusters:
Idents(seu.q) <- 'Level3'
ClusterMarkers <- FindAllMarkers(seu.q)
top3 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=3, wt = avg_log2FC)
DoHeatmap(seu.q, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)
write.csv(output_path,"ClusterMarkersLevel3all.csv")
Same cell type markers are overlapping
# error in dot plot because of repeat gene
marker.top3 <- unique(top3$gene)
DotPlot(seu.q, features = marker.top3) + RotatedAxis()
# RG-NPC is similar to mix and DAneurons2
Not all astros overlap with astro markers and not all neurons overlap neurons The RG overlap with all kinds of cells.
Subset out populations for some label transfers from the public data.
Subset out Neurons
# subset neurons
# Level2
cluster.ids <- c("Astro-RG-Pre","Neurons1-im","RG1-NPC","Astro1","Neurons2-im",
"Astro-NPC","Astro","Neurons3","RG2","Neurons4",
"Astro3","Neurons5","RG3-NPC","Neurons6-DA","Neurons7-NPC",
"Mix-RG4","RG5","Endothelial-NPC","Neurons8-inh-max","Neurons9-DA",
"Neurons10-DA","RG6","RG7-epi","NPC-Astro","RG-OPC"
)
# Level 3
cluster.ids <- c("Astro1","Neurons1","RG1","Astro2","Neurons2",
"Astro3","Astro4","Neurons3","RG2","Neurons4",
"Astro5","Neurons5","RG3-NPC","DANeurons1","Neurons6",
"Mix","RG4","Endothelial-NPC","Neurons8-inh-max","DANeurons2",
"DANeurons3","RG5","RG6-epi","RG7","RG-OPC"
)
Idents(seu.q) <- 'Level3'
neuron.id <- c("Neurons1","Neurons2","Neurons3","Neurons4",
"Neurons5","DANeurons1","Neurons6",
"Mix","Neurons8-inh-max","DANeurons2",
"DANeurons3")
# without mix
neuron.id <- c("Neurons1","Neurons2","Neurons3","Neurons4",
"Neurons5","DANeurons1","Neurons6",
"Neurons8-inh-max","DANeurons2",
"DANeurons3")
neurons.sub <- subset(seu.q, idents = neuron.id)
table(neurons.sub$Level3)
DimPlot(neurons.sub, label = TRUE)
Find Cluster markers to distinguish Neuron subgroups
table(neurons.sub$Level3)
Idents(neurons.sub) <- 'Level3'
ClusterMarkers <- FindAllMarkers(neurons.sub, only.pos = TRUE)
top3 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=3, wt = avg_log2FC)
DoHeatmap(neurons.sub, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)
marker.top3 <- unique(top3$gene)
DotPlot(neurons.sub, features = marker.top3) + RotatedAxis()
write.csv(ClusterMarkers, paste(output_path,"ClusterMarkersLevel3NeuronSubnoMix.csv", sep = ""))
Neurons 1 has overlapping markers with DAneurons2 Neurons 2, Neurons 4, Neurons 6 are very similar in the top markers - these are all in the Neurons1 FACS pop And the top markers are mito genes.
Look at the DA marker genes and see if there are differences there Also some neuronal markers
DA neurons 1 has the most different DA markers (TH, CALB1, SLC17A6, OTX2) - highest CALB1 DA neurons 2 has most cells and highest level of TH expression, most cells expressing SOX2 at a low levels DA neurons 3 SLC17A6, OTX2, OTX2 highest
Get markers for just DA neurons
Idents(neurons.sub) <- 'Level3'
DA.sub <- subset(neurons.sub, idents = c("DANeurons1","DANeurons2","DANeurons3"))
ClusterMarkers.DA <- FindAllMarkers(DA.sub, only.pos = TRUE)
top3 <- ClusterMarkers.DA %>% group_by(cluster) %>% top_n(n=3, wt = avg_log2FC)
DoHeatmap(DA.sub, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)
marker.top3 <- unique(top3$gene)
DotPlot(neurons.sub, features = marker.top3) + RotatedAxis()
DotPlot(DA.sub, features = marker.top3) + RotatedAxis()
write.csv(ClusterMarkers, paste(output_path,"ClusterMarkersDANeurons.csv", sep = ""))
Good markers DA1 - RAB3B DA2 - TPBG or HES1 - TPBG is proposed as a DA marker (HES1 midbrain patterning) DA3 - TPH1 - sertononergic neurons or TTR oxidative stress protective
Compare the other neurons and find markers
DoHeatmap(neur.sub, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)
marker.top3 <- unique(top3$gene)
DotPlot(neurons.sub, features = marker.top3) + RotatedAxis()
DotPlot(neur.sub, features = marker.top3) + RotatedAxis()
write.csv(ClusterMarkers.neur, paste(output_path,"ClusterMarkersotherNeurons.csv", sep = ""))
Have a look at some markers in feature plots
DimPlot(neur.sub)
FeaturePlot(neur.sub, features = c("RUNX1T1", "ASCL1","GAD1","GRIN2A","GRIA2","VAMP2"))
mat_neuron = c("RBFOX3","SYP","DLG45","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6")
# NeuN is FOX3 - RBFOX3
# PSD95 also SP-90 or DLG4
# VGLUT2 is SLC17A6
#DoHeatmap(seu.q, features = mat_neuron, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
# cluster 4 also show mature neuron markers
DotPlot(neur.sub, features = mat_neuron)+RotatedAxis()
Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
The following requested variables were not found: DLG45
# excitatory neuron markers
ex = c("GRIA2","GRIA1","GRIA4","GRIN1","GRIN2B","GRIN2A","GRIN3A","GRIN3","GRIP1","CAMK2A")
#DoHeatmap(seu.q, features = ex, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(neur.sub, features = ex)+RotatedAxis()
Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
The following requested variables were not found: GRIN3
# inhibitory neuron markers
inh = c("GAD1","GAD2", "GAT1","PVALB","GABR2","GABR1","GBRR1","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
DotPlot(neur.sub, features = inh) + RotatedAxis()
Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
The following requested variables were not found: GAT1, GABR2, GABR1, GBRR1
I’ll separate out Neurons 2,4,6
Idents(neurons.sub) <- 'Level3'
neurons1.sub <- subset(neurons.sub, idents = c("Neurons2","Neurons4","Neurons6"))
ClusterMarkers.n1 <- FindAllMarkers(neurons1.sub, only.pos = TRUE)
Calculating cluster Neurons2
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Calculating cluster Neurons4
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top3 <- ClusterMarkers.n1 %>% group_by(cluster) %>% top_n(n=3, wt = avg_log2FC)
DoHeatmap(neurons1.sub, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)
marker.top3 <- unique(top3$gene)
DotPlot(neurons1.sub, features = marker.top3) + RotatedAxis()
Warning: Scaling data with a low number of groups may produce misleading results
DotPlot(DA.sub, features = marker.top3) + RotatedAxis()
Error in UseMethod(generic = "DefaultAssay", object = object) :
no applicable method for 'DefaultAssay' applied to an object of class "character"
Idents(neur.sub) <- 'Level3'
neurons2.sub <- subset(neur.sub, idents = c("Neurons2","Neurons4","Neurons6"), invert = TRUE)
ClusterMarkers.n2 <- FindAllMarkers(neurons2.sub, only.pos = TRUE)
Calculating cluster Neurons1
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|++++++ | 10% ~01s
|++++++ | 12% ~01s
|+++++++ | 13% ~01s
|++++++++ | 15% ~01s
|+++++++++ | 16% ~01s
|+++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 28% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++ | 31% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++ | 34% ~00s
|++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++ | 37% ~00s
|++++++++++++++++++++ | 39% ~00s
|+++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++ | 42% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|++++++++++++++++++++++++ | 48% ~00s
|+++++++++++++++++++++++++ | 49% ~00s
|++++++++++++++++++++++++++ | 51% ~00s
|+++++++++++++++++++++++++++ | 52% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
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|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s
Calculating cluster Neurons3
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 4 % ~01s
|+++ | 5 % ~01s
|++++ | 6 % ~01s
|++++ | 7 % ~01s
|+++++ | 9 % ~01s
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|++++++++ | 15% ~01s
|++++++++ | 16% ~01s
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|++++++++++++++++++++++++ | 46% ~01s
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Calculating cluster Neurons5
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 3 % ~01s
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Calculating cluster Neurons8-inh-max
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~02s
|++ | 3 % ~02s
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top3 <- ClusterMarkers.n2 %>% group_by(cluster) %>% top_n(n=3, wt = avg_log2FC)
DoHeatmap(neurons2.sub, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)
marker.top3 <- unique(top3$gene)
DotPlot(neurons2.sub, features = marker.top3) + RotatedAxis()
Warning: Scaling data with a low number of groups may produce misleading results
write.csv(ClusterMarkers.n2, paste(output_path,"ClusterMarkersNeurons1358.csv", sep = ""))
Look at the GO terms when separating out the neuron subtypes
group = "Neurons3"
#for(group in group.list){
print(group)
[1] "Neurons3"
Up.list <- ClusterMarkers.n2 %>% filter(cluster == group & avg_log2FC > 0)
genes <- Up.list$gene
Er <- enrichr(genes, databases = db)
Uploading data to Enrichr... Done.
Querying GO_Cellular_Component_2015... Done.
Querying GO_Biological_Process_2015... Done.
Querying CellMarker_Augmented_2021... Done.
Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.
print(plotEnrich(Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
t.GOcell <- Er[[1]] %>% select(Term, Genes, Combined.Score)
print(t.GOcell)
t.GObio <- Er[[2]] %>% select(Term, Genes, Combined.Score)
print(t.GObio)
t.CellMarker <- Er[[3]] %>% select(Term, Genes, Combined.Score)
print(t.CellMarker)
t.Azi <- Er[[4]] %>% select(Term, Genes, Combined.Score)
print(t.Azi)
#}
Compare the neurons1 groups to the neurons2 groups
Check the GO terms and celltype libraries for the two groups of neurons
down.list <- ClusterMarkers %>% filter(avg_log2FC < 0)
genes <- rownames(down.list)
Er <- enrichr(genes, databases = db)
Uploading data to Enrichr... Done.
Querying GO_Cellular_Component_2015... Done.
Querying GO_Biological_Process_2015... Done.
Querying CellMarker_Augmented_2021... Done.
Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.
print(plotEnrich(Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
t.GOcell <- Er[[1]] %>% select(Term, Genes, Combined.Score)
print(t.GOcell)
t.GObio <- Er[[2]] %>% select(Term, Genes, Combined.Score)
print(t.GObio)
t.CellMarker <- Er[[3]] %>% select(Term, Genes, Combined.Score)
print(t.CellMarker)
t.Azi <- Er[[4]] %>% select(Term, Genes, Combined.Score)
print(t.Azi)
Compare directly Neurons 1 (low CD24) with Neurons 2 (high CD24) For DEG and cell type predictions
seu <- neurons.1.2
seu <- NormalizeData(seu, normalization.method = "LogNormalize", scale.factor = 10000)
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
seu <- ScaleData(seu)
Centering and scaling data matrix
|
| | 0%
|
|============================================== | 50%
|
|============================================================================================| 100%
seu.q <- RunPCA(seu)
PC_ 1
Positive: DLK1, CLU, SPARCL1, VIM, PTGDS, GNG11, FTL, TFPI2, APOE, WIF1
S100A10, IFITM2, HPD, IGFBP5, GDF10, CP, ID3, SLCO1C1, CYP1B1, MGST1
PAPPA2, NUPR1, ANXA1, SCRG1, PON2, S100A6, PTN, MT-ND2, RBP4, TTYH1
Negative: CELF4, CHGB, SOX4, ANK3, CACNA2D1, PCLO, INA, PCDH9, SYT1, SSTR2
STMN2, STMN1, OCIAD2, TENM3, KLC1, GRIA2, AMER2, DYNC1I1, STMN4, DCX
ZFHX4, NRXN1, NEUROD1, ATCAY, NCKAP5, TERF2IP, PTPRR, MARCH1, EOMES, NREP
PC_ 2
Positive: ASCL1, TMSB4X, CD24, TPBG, RUNX1T1, PAX6, CMIP, CCNG2, FOS, EGR1
DDC, CDH7, PTMA, GPHN, ZIC2, KCNQ1OT1, EFNB2, TEAD1, CADM2, ZIC1
CHD7, DSP, CDKN1C, JUNB, HES6, ZNF385D, STXBP5L, RNASEH2B, SOX4, PLCG2
Negative: MT-CO1, MT-ATP6, MT-CO2, MT-CYB, MT-CO3, MT-ND2, MT-ND3, MT-ND4, MT-ND1, MT-ND5
MALAT1, MT-ND4L, MT-ND6, PLPPR4, NEUROD1, IMPG2, PCAT4, PTPRR, SST, FAM19A4
AANAT, BCAT1, GSG1, TPH1, SGIP1, OLFM3, TTR, RBFOX1, BTBD8, TRIM9
PC_ 3
Positive: PCAT4, TPH1, IMPG2, NCKAP5, FAM19A4, PLPPR4, BTBD8, GSG1, BCAT1, GNB3
NEUROD1, SST, AANAT, GK5, RBFOX1, CLSTN2, LHX4, ANKRD33B, CRABP2, ETV3L
TRIM9, AIPL1, CHRM3, AC004852.2, PRKG2, TMSB4X, NTM, OLFM3, SLC1A2, AP000459.1
Negative: MT-ATP6, MT-CO2, MT-CO1, MT-ND3, MT-CO3, MT-CYB, MT-ND4, MT-ND2, MT-ND1, MT-ND5
EOMES, RASGRP1, MGAT4C, RAB3B, ELAVL3, ELAVL4, MT-ND4L, TSHZ2, SCN9A, ATP8A2
LHX1, PTPRO, SLIT1, MALAT1, ADCYAP1, RGS4, KRT222, SLC16A12, BDNF, GRIA1
PC_ 4
Positive: TFPI2, LY6H, PEG10, SCG2, RASGRP1, S100A10, EOMES, TSHZ2, DNER, VIM
ELAVL4, RFX4, PHLDA1, ANXA1, DYNC1I1, MGAT4C, SLC16A12, PPP1R14C, ATP8A2, PTPRO
PTPRZ1, ID4, MGST1, GNG11, CLU, CHGB, ACSL4, NTRK2, KRT222, STMN2
Negative: MT-CYB, MT-ATP6, MT-ND2, MT-CO1, MT-CO3, MT-CO2, MT-ND3, MT-ND4, MT-ND1, MT-ND5
DDC, AFF2, CDHR1, PROM1, MT-ND4L, MALAT1, PPP1R1B, THSD7A, RUNX1T1, CBLN2
RNASEH2B, MT-ND6, PTGDS, PCDH17, PROX1, CDKN1C, ADRA2C, CDH7, ASCL1, DSP
PC_ 5
Positive: SLC17A6, SOX4, CHGB, TMSB4X, GK5, EOMES, CELF4, PTMA, HES6, KHDRBS2
TOX3, STMN1, NEUROD2, PRDM8, BHLHE22, NEUROD1, NREP, PTPRO, CACNA2D1, MGP
FABP7, CADPS, EGR1, DYNC1I1, TPBG, ADCYAP1, CYP1B1, INSM1, SEMA3A, NPS
Negative: PTN, TFPI2, S100A10, DLK1, LY6H, PON2, VIM, GNG11, MGST1, CLU
TTYH1, SCG2, IFI27, ITM2C, ANXA1, RFX4, SPARCL1, SERPINI1, FTL, FGFBP1
ITPR2, GALNT1, APOE, NTRK2, PEG10, COL1A1, SCRG1, GRIN2A, PGA5, SLC2A1
DimPlot(seu.q)
Idents(seu.q) <- 'Level3'
cluster.ids <- c("Neurons-CD24","Neurons","Neurons-CD24","Neurons",
"Neurons-CD24","DAN",
"Neurons","Neurons-CD24","DAN","DAN"
)
names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$NeuronGroups <- Idents(seu.q)
DimPlot(seu.q, group.by = 'Level3', split.by = 'orig.ident')
DimPlot(seu.q, group.by = 'NeuronGroups', split.by = 'orig.ident')
See the predicted markers
Double check and rename the two neuron populations
FeaturePlot(neurons.sub, features = c("CD24","NCAM1", "MAP2", "TUBB3"), split.by = "orig.ident")
Warning in FeaturePlot(neurons.sub, features = c("CD24", "NCAM1", "MAP2", :
All cells have the same value (0) of TUBB3.
Look at cell marker libraries and GO terms in all neurons contrasts
library(enrichR)
setEnrichrSite("Enrichr") # Human genes
# list of all the databases
# libaries with cell types
dbs <- listEnrichrDbs()
dbs
db <- c('GO_Cellular_Component_2015','GO_Biological_Process_2015',
'CellMarker_Augmented_2021','Azimuth_Cell_Types_2021')
# enrichr(genes, databases = NULL)
group.list <- as.character(unique(ClusterMarkers$cluster))
# try to run a loop
# the loop runs but it's hard to tell the output
# need to run one at a time
[1] "Neurons1" "Neurons2" "Neurons3" "Neurons4" "Neurons5"
[6] "DANeurons1" "Neurons6" "Mix" "Neurons8-inh-max" "DANeurons2"
[11] "DANeurons3"
group.list = "DANeurons3"
for(group in group.list){
print(group)
Up.list <- ClusterMarkers %>% filter(cluster == group & avg_log2FC > 0)
genes <- Up.list$gene
Er <- enrichr(genes, databases = db)
print(plotEnrich(Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
t.GOcell <- Er[[1]] %>% select(Term, Genes, Combined.Score)
print(t.GOcell)
t.GObio <- Er[[2]] %>% select(Term, Genes, Combined.Score)
print(t.GObio)
t.CellMarker <- Er[[3]] %>% select(Term, Genes, Combined.Score)
print(t.CellMarker)
t.Azi <- Er[[4]] %>% select(Term, Genes, Combined.Score)
print(t.Azi)
}
Label transfer Kamath DA neurons in Neurons Subset (has both DA neurons and Others as predicted by AIW002 label transfers)
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data/DAsubgroups_processed.Rds")
Idents(seu.r) <- "Cell_Subtype"
Idents(neurons.sub) <- "Level3"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = neurons.sub, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$Cell_Subtype, k.weight = 30)
neurons.sub <- AddMetaData(neurons.sub, predictions$predicted.id, col.name = "da.pred")
neurons.sub <- AddMetaData(neurons.sub, predictions$prediction.score.max, col.name = "prediction.score.max")
table(neurons.sub$da.pred)
neurons.sub$da.pred.thresh <- ifelse(neurons.sub$prediction.score.max > 0.55, neurons.sub$da.pred, "none")
DimPlot(neurons.sub, group.by = 'da.pred.thresh')
table(neurons.sub$da.pred.thresh)
See the predictions from Kamath DA subtypes
#DA subtypes
# all predicitions
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$da.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
# with threshold
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$da.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.thresh <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top.thresh) <- NULL
df.top.thresh$I <- row.names(df.top.thresh)
Label from Bhadari adult brain -all brain down sampled
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Karolinski_DevForebrain_downsample_Level1.RDS")
Idents(seu.r) <- "Level1"
Idents(neurons.sub) <- "Level3"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = neurons.sub, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$Level1, k.weight = 50)
neurons.sub <- AddMetaData(neurons.sub, predictions$predicted.id, col.name = "Bha.pred")
neurons.sub <- AddMetaData(neurons.sub, predictions$prediction.score.max, col.name = "prediction.score.max")
table(neurons.sub$Bha.pred)
neurons.sub$Bha.pred.thresh <- ifelse(neurons.sub$prediction.score.max > 0.80, neurons.sub$Bha.pred, "none")
DimPlot(neurons.sub, group.by = 'Bha.pred.thresh')
table(neurons.sub$Bha.pred.thresh)
#Whole adult brain
# all predicitions
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$Bha.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
# with threshold
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$Bha.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.thresh <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top.thresh) <- NULL
df.top.thresh$I <- row.names(df.top.thresh)
Try with only the midbrain and striatum Bhaduri
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Bhaduri_midbrain_striatum.RDS")
Idents(seu.r) <- "cell_cluster"
Idents(neurons.sub) <- "Level3"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = neurons.sub, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$cell_cluster, k.weight = 50)
neurons.sub <- AddMetaData(neurons.sub, predictions$predicted.id, col.name = "Bha.m.pred")
neurons.sub <- AddMetaData(neurons.sub, predictions$prediction.score.max, col.name = "prediction.score.max")
table(neurons.sub$Bha.m.pred)
neurons.sub$Bha.m.pred.thresh <- ifelse(neurons.sub$prediction.score.max > 0.70, neurons.sub$Bha.m.pred, "none")
DimPlot(neurons.sub, group.by = 'Bha.m.pred.thresh')
table(neurons.sub$Bha.m.pred.thresh)
# all predicitions
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$Bha.m.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
# with threshold
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$Bha.m.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.thresh <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top.thresh) <- NULL
df.top.thresh$I <- row.names(df.top.thresh)
Use the developing forbrain dataset Karolinski
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Karolinski_DevForebrain_downsample_Level1.RDS")
Idents(seu.r) <- "Clusters"
Idents(neurons.sub) <- "Level3"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = neurons.sub, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$Clusters, k.weight = 50)
neurons.sub <- AddMetaData(neurons.sub, predictions$predicted.id, col.name = "K.pred")
neurons.sub <- AddMetaData(neurons.sub, predictions$prediction.score.max, col.name = "prediction.score.max")
table(neurons.sub$K.pred)
neurons.sub$K.pred.thresh <- ifelse(neurons.sub$prediction.score.max > 0.70, neurons.sub$K.pred, "none")
DimPlot(neurons.sub, group.by = 'K.pred.thresh')
table(neurons.sub$K.pred.thresh)
# all predicitions
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$K.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
# with threshold
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$K.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.thresh <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top.thresh) <- NULL
df.top.thresh$I <- row.names(df.top.thresh)
Nowakowski developing cortex
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Nowakowski_dev_cortext.RDS")
Idents(seu.r) <- "WGCNAcluster"
Idents(neurons.sub) <- "Level3"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = neurons.sub, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$WGCNAcluster, k.weight = 50)
neurons.sub <- AddMetaData(neurons.sub, predictions$predicted.id, col.name = "N.pred")
neurons.sub <- AddMetaData(neurons.sub, predictions$prediction.score.max, col.name = "prediction.score.max")
table(neurons.sub$N.pred)
neurons.sub$N.pred.thresh <- ifelse(neurons.sub$prediction.score.max > 0.70, neurons.sub$N.pred, "none")
DimPlot(neurons.sub, group.by = 'N.pred.thresh')
table(neurons.sub$N.pred.thresh)
# all predicitions
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$N.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
# with threshold
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$N.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.thresh <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top.thresh) <- NULL
df.top.thresh$I <- row.names(df.top.thresh)
Check the DA neurons and None-neurons in the Kamath
neurons.sub$neur.pred.thresh <- ifelse(neurons.sub$prediction.score.max > 0.45, neurons.sub$neur.pred, "none")
DimPlot(neurons.sub, group.by = 'neur.pred.thresh')
table(neurons.sub$neur.pred.thresh)
Ex_LAMP5_BAIAP3 none SOX6_DDT
357 2374 615
# all predictions
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$neur.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
# with threshold
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$neur.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.thresh <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top.thresh) <- NULL
df.top.thresh$I <- row.names(df.top.thresh)
Label again after more transfer labels
Subset out the Astrocytes for annotation
Idents(seu.q) <- 'Level3'
# Level 3
cluster.ids <- c("Astro1","Neurons1","RG1","Astro2","Neurons2",
"Astro3","Astro4","Neurons3","RG2","Neurons4",
"Astro5","Neurons5","RG3-NPC","DANeurons1","Neurons6",
"Mix","RG4","Endothelial-NPC","Neurons8-inh-max","DANeurons2",
"DANeurons3","RG5","RG6-epi","RG7","RG-OPC"
)
astro.list <- c("Astro1","Astro2","Astro3","Astro4",
"Astro5","Mix"
)
astro.sub <- subset(seu.q, idents = astro.list)
table(astro.sub$Level3)
DimPlot(astro.sub, label = TRUE, repel = TRUE)
Identify markers of astrocyte subgroups
Idents(seu.q) <- 'Level3'
ClusterMarkers <- FindAllMarkers(astro.sub)
top3 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=3, wt = avg_log2FC)
DoHeatmap(astro.sub, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)
marker.top3 <- unique(top3$gene)
DotPlot(astro.sub, features = marker.top3) + RotatedAxis()
write.csv(ClusterMarkers, paste(output_path,"ClusterMarkersLevel3AstroSub.csv"))
Astro 1 and 2, Astro 3 and 4 are similar in makers. These might be merged.
Find the markers between astro 3 and 4 only
Idents(seu.q) <- 'Level3'
ClusterMarkers34 <- FindMarkers(astro.sub, ident.1 = "Astro3", ident.2 = "Astro4", only.pos = TRUE)
ClusterMarkers34 <- FindMarkers(astro.sub, ident.1 = "Astro4", ident.2 = "Astro3", only.pos = TRUE)
top.up <- ClusterMarkers34 %>% top_n(n=5, wt = avg_log2FC)
top.down <- ClusterMarkers34 %>% top_n(n=-5, wt = avg_log2FC)
DoHeatmap(astro.sub, features = c(rownames(top.up),rownames(top.down)), size=3, angle =90, group.bar.height = 0.02)
# these are all up in astro4
up.down <- c("SPINT2", "RGCC", "NNMT", "TRA2B", "BDP1",
"DDR2", "COPB2", "SELENOS", "ALDH3A2", "DDX42", "CP", "CPVL", "ARL4C")
# the expression was all low, higher in mixed
# no good markers
# up in astro3 but not significant
up.down <- c("ATP1A2", "PTN", "APOD", "DLK1", "PABPN1", "TTYH1", "CDO1")
DotPlot(astro.sub, features = up.down) + RotatedAxis()
# PTN is up in Astro3
I will merge Astro3 and Astro4 Maybe 1 and 2 also need to be merged
Idents(astro.sub) <- 'Level3'
ClusterMarkers12 <- FindMarkers(astro.sub, ident.1 = "Astro1", ident.2 = "Astro2", only.pos = TRUE)
ClusterMarkers21 <- FindMarkers(astro.sub, ident.1 = "Astro2", ident.2 = "Astro1", only.pos = TRUE)
# astro2 up
up.down <- c("ESM1", "RSPO2", "HTRA1", "KCNQ10T1", "SOX2", "CCSER2", "CALD1", "PJA2")
DotPlot(astro.sub, features = up.down) + RotatedAxis()
# good markers astro2: ESM1, SOX2, PJA2
# astro1 up not significant
up.down <- c("NDUFAB1", "RPS29", "ATP5PO", "RNF5", "HDDC2", "POLR1D", "FAM229B", "EEF1D")
DotPlot(astro.sub, features = up.down) + RotatedAxis()
# not good marker for astro 1
I’ll merge astro1 and astro2, also astro3 and astro4
Idents(seu.q) <- 'RNA_snn_res.1.8'
#Idents(seu.q) <- 'Level3'
# Level 3
# merge astro1 + astro2 = astro1, astro3+ astro4 = astro2, now astro5 = astro3
cluster.ids <- c("Astro1","Neurons1","RG1","Astro1","Neurons2",
"Astro2","Astro2","Neurons3","RG2","Neurons4",
"Astro3","Neurons5","RG3-NPC","DANeurons1","Neurons6",
"Mix","RG4","Endothelial-NPC","Neurons8-inh-max","DANeurons2",
"DANeurons3","RG5","RG6-epi","RG7","RG-OPC"
)
names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$Level4 <- Idents(seu.q)
astro.list <- c("Astro1","Astro2","Astro3","Mix"
)
astro.sub <- subset(seu.q, idents = astro.list)
table(astro.sub$Level4)
DimPlot(astro.sub, group.by = "Level4")
# cluster markers with new Astro groupings
Idents(seu.q) <- 'Level4'
ClusterMarkers <- FindAllMarkers(astro.sub, only.pos = TRUE)
top3 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(astro.sub, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)
marker.top3 <- unique(top3$gene)
DotPlot(astro.sub, features = marker.top3) + RotatedAxis()
write.csv(ClusterMarkers, paste(output_path,"ClusterMarkersLevel4AstroSub.csv"))
### how will this look without mix?
astro.list <- c("Astro1","Astro2","Astro3"
)
astro.sub2 <- subset(seu.q, idents = astro.list)
table(astro.sub2$Level4)
DimPlot(astro.sub2, group.by = "Level4")
Idents(seu.q) <- 'Level4'
ClusterMarkers <- FindAllMarkers(astro.sub2, only.pos = TRUE)
top3 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(astro.sub2, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)
marker.top3 <- unique(top3$gene)
DotPlot(astro.sub2, features = marker.top3) + RotatedAxis()
write.csv(ClusterMarkers, paste(output_path,"ClusterMarkersLevel4AstroSubnoMix.csv"))
There are clearer markers without the mix population
Check the GO terms in the astrocyte subtypes
library(enrichR)
setEnrichrSite("Enrichr") # Human genes
# list of all the databases
# libaries with cell types
dbs <- listEnrichrDbs()
dbs
db <- c('GO_Cellular_Component_2015','GO_Biological_Process_2015',
'CellMarker_Augmented_2021','Azimuth_Cell_Types_2021')
# enrichr(genes, databases = NULL)
group.list <- as.character(unique(ClusterMarkers$cluster))
group <- "Astro1"
#for(group in group.list){
print(group)
Up.list <- ClusterMarkers %>% filter(cluster == group & avg_log2FC > 0)
genes <- Up.list$gene
Er <- enrichr(genes, databases = db)
print(plotEnrich(Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
t.GOcell <- Er[[1]] %>% select(Term, Genes, Combined.Score)
print(t.GOcell)
t.GObio <- Er[[2]] %>% select(Term, Genes, Combined.Score)
print(t.GObio)
t.CellMarker <- Er[[3]] %>% select(Term, Genes, Combined.Score)
print(t.CellMarker)
t.Azi <- Er[[4]] %>% select(Term, Genes, Combined.Score)
print(t.Azi)
#}
Predict the Astrocyte subgroups
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Nowakowski_dev_cortext.RDS")
Idents(seu.r) <- "WGCNAcluster"
Idents(astro.sub2) <- "Level4"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = astro.sub2, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$WGCNAcluster, k.weight = 50)
astro.sub2 <- AddMetaData(astro.sub2, predictions$predicted.id, col.name = "N.pred")
astro.sub2 <- AddMetaData(astro.sub2, predictions$prediction.score.max, col.name = "prediction.score.max")
table(astro.sub2$N.pred)
astro.sub2$N.pred.thresh <- ifelse(astro.sub2$prediction.score.max > 0.50, astro.sub2$N.pred, "none")
DimPlot(astro.sub2, group.by = 'N.pred.thresh')
table(astro.sub2$N.pred.thresh)
# all predicitions
t.lables <- as.data.frame(table(astro.sub2$Level4, astro.sub2$N.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
# with threshold
t.lables <- as.data.frame(table(astro.sub2$Level4, astro.sub2$N.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.thresh <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top.thresh) <- NULL
df.top.thresh$I <- row.names(df.top.thresh)
Predictions developing forebrain
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Karolinski_DevForebrain_downsample_Level1.RDS")
Idents(seu.r) <- "Clusters"
Idents(astro.sub2) <- "Level4"
astro.sub2 <- ScaleData(astro.sub2)
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = astro.sub2, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$Clusters, k.weight = 30)
astro.sub2 <- AddMetaData(astro.sub2, predictions$predicted.id, col.name = "k.pred")
astro.sub2 <- AddMetaData(astro.sub2, predictions$prediction.score.max, col.name = "prediction.score.max")
table(astro.sub2$k.pred)
astro.sub2$k.pred.thresh <- ifelse(astro.sub2$prediction.score.max > 0.70, astro.sub2$k.pred, "none")
DimPlot(astro.sub2, group.by = 'N.pred.thresh')
table(astro.sub2$k.pred.thresh)
# all predicitions
t.lables <- as.data.frame(table(astro.sub2$Level4, astro.sub2$k.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
# with threshold
t.lables <- as.data.frame(table(astro.sub2$Level4, astro.sub2$k.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.thresh <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top.thresh) <- NULL
df.top.thresh$I <- row.names(df.top.thresh)
Kamath astrocyte subgroups
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data/PD_astro.Rds")
Idents(seu.r) <- "Cell_Subtype"
Idents(astro.sub2) <- "Level4"
seu.r <- NormalizeData(seu.r, normalization.method = "LogNormalize", scale.factor = 10000)
seu.r <- FindVariableFeatures(seu.r, selection.method = "vst", nfeatures = 2000)
seu.r <- ScaleData(seu.r)
seu.r <- RunPCA(seu.r)
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = astro.sub2, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$Cell_Subtype, k.weight = 30)
astro.sub2 <- AddMetaData(astro.sub2, predictions$predicted.id, col.name = "Mas.pred")
astro.sub2 <- AddMetaData(astro.sub2, predictions$prediction.score.max, col.name = "prediction.score.max")
table(astro.sub2$Mas.pred)
astro.sub2$Mas.pred.thresh <- ifelse(astro.sub2$prediction.score.max > 0.90, astro.sub2$Mas.pred, "none")
DimPlot(astro.sub2, group.by = 'Mas.pred.thresh')
table(astro.sub2$Mas.pred.thresh)
# all predicitions
t.lables <- as.data.frame(table(astro.sub2$Level4, astro.sub2$Mas.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
# with threshold
t.lables <- as.data.frame(table(astro.sub2$Level4, astro.sub2$Mas.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.thresh <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top.thresh) <- NULL
df.top.thresh$I <- row.names(df.top.thresh)
Adult brain midbrain and striatum
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Bhaduri_midbrain_striatum.RDS")
Idents(seu.r) <- "cell_cluster"
Idents(astro.sub2) <- "Level4"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = astro.sub2, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$cell_cluster, k.weight = 30)
# note that if there are not enough anchors you need to reduce the k.weight from default 50
astro.sub2 <- AddMetaData(astro.sub2, predictions$predicted.id, col.name = "Bha.pred")
astro.sub2 <- AddMetaData(astro.sub2, predictions$prediction.score.max, col.name = "prediction.score.max")
table(astro.sub2$Bha.pred)
astro.sub2$Bha.pred.thresh <- ifelse(astro.sub2$prediction.score.max > 0.90, astro.sub2$Bha.pred, "none")
DimPlot(astro.sub2, group.by = 'Bha.pred.thresh')
table(astro.sub2$Bha.pred.thresh)
# all predicitions
t.lables <- as.data.frame(table(astro.sub2$Level4, astro.sub2$Bha.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
# with threshold
t.lables <- as.data.frame(table(astro.sub2$Level4, astro.sub2$Bha.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.thresh <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top.thresh) <- NULL
df.top.thresh$I <- row.names(df.top.thresh)
Will be best to choose some subtype markers Astro1 - most predicted as astrocytes, has GO terms differentation Astro2 - GO terms involving extracellular matrix Astro3 - Go terms involving cell adhsion and proteins
Subset Radial Glia to get subtypes
First calculate DGE and determine if some subgroups should be merged
DoHeatmap(RG.sub, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)
Warning in DoHeatmap(RG.sub, features = top3$gene, size = 3, angle = 90, :
The following features were omitted as they were not found in the scale.data slot for the RNA assay: RPL21, RPL34
marker.top3 <- unique(top3$gene)
DotPlot(RG.sub, features = marker.top3) + RotatedAxis()
output_path <- "/Users/rhalenathomas/Documents/Projects_Papers/PhenoID/ForFigures/scRNA/"
write.csv(ClusterMarkers, paste(output_path,"ClusterMarkersRG.csv"))
Many significant markers but not a large LFC for any Indeed the marker genes are highly overlapping between many groups RG7 and RG-OPC are distinctive from the others
RG1 markers are expressed in all groups RG1,RG2,RG3,RG4 are all very overlapping RG5 RG6 are overlapping
RG5 and RG6 aren’t even in the Radial Glia sorted population
Subset just the original RG population and look for markers there
Idents(RG.sub) <- 'orig.ident'
RG.FACS <- subset(RG.sub, idents = "RadialGlia")
DimPlot(RG.FACS, group.by = 'Level3')
NA
NA
Get markers for these groups
Maybe RG1 and RG2 should be merged
clustermarkers.2.4 <- FindMarkers(RG.FACS, ident.1 = "RG2", ident.2 = "RG4")
| | 0 % ~calculating
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No positive markers for RG1 vs RG2 however there are lots of up regulated markers in RG2
Subset out RG1-4
Idents(RG.sub) <- 'Level3'
RG.sub2 <- subset(RG.sub, idents = c("RG1","RG2","RG3-NPC","RG4"))
Idents(RG.sub2) <- 'Level3'
ClusterMarkers <- FindAllMarkers(RG.sub2, only.pos = TRUE)
Calculating cluster RG1
| | 0 % ~calculating
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Calculating cluster RG2
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Calculating cluster RG3-NPC
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Calculating cluster RG4
| | 0 % ~calculating
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|++++++++++++++++++++++++ | 47% ~02s
|++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|+++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|+++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s
View(clustermarkers.1.2)
top3 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=3, wt = avg_log2FC)
DoHeatmap(RG.sub2, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)
Warning in DoHeatmap(RG.sub2, features = top3$gene, size = 3, angle = 90, :
The following features were omitted as they were not found in the scale.data slot for the RNA assay: RPL34, RPS12
marker.top3 <- unique(top3$gene)
DotPlot(RG.sub2, features = marker.top3) + RotatedAxis()
Warning: Scaling data with a low number of groups may produce misleading results
top.10 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=10, wt = avg_log2FC)
RG2 and 4 are also overlapping in marker expression - but in 2 vs 4 there are positive markers in both directions
I will merge RG2 and RG4
Test if RG5 and RG6 should be merged into one group
clustermarkers.5.6 <- FindMarkers(RG.sub, ident.1 = "RG5", ident.2 = "RG6-epi")
| | 0 % ~calculating
|+ | 1 % ~10s
|++ | 2 % ~06s
|++ | 3 % ~04s
|+++ | 4 % ~04s
|+++ | 5 % ~03s
|++++ | 6 % ~03s
|++++ | 7 % ~03s
|+++++ | 8 % ~03s
|+++++ | 9 % ~03s
|++++++ | 10% ~03s
|++++++ | 11% ~02s
|+++++++ | 12% ~02s
|+++++++ | 13% ~02s
|++++++++ | 14% ~02s
|++++++++ | 15% ~02s
|+++++++++ | 16% ~02s
|+++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|+++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s
There are very different and shouldn’t be merged
Check the cluster markers again
Idents(RG.sub) <- 'Level4'
ClusterMarkers <- FindAllMarkers(RG.sub, only.pos = TRUE)
Calculating cluster RG1
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~03s
|++ | 3 % ~03s
|+++ | 4 % ~03s
|+++ | 5 % ~03s
|++++ | 6 % ~03s
|++++ | 7 % ~03s
|+++++ | 8 % ~03s
|+++++ | 9 % ~03s
|++++++ | 10% ~03s
|++++++ | 11% ~03s
|+++++++ | 12% ~03s
|+++++++ | 13% ~03s
|++++++++ | 14% ~03s
|++++++++ | 15% ~02s
|+++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|++++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|+++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|++++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|++++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|+++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|+++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s
Calculating cluster RG2
| | 0 % ~calculating
|+ | 1 % ~06s
|++ | 2 % ~06s
|++ | 3 % ~06s
|+++ | 4 % ~06s
|+++ | 5 % ~06s
|++++ | 6 % ~05s
|++++ | 7 % ~05s
|+++++ | 9 % ~05s
|+++++ | 10% ~05s
|++++++ | 11% ~05s
|++++++ | 12% ~05s
|+++++++ | 13% ~05s
|+++++++ | 14% ~05s
|++++++++ | 15% ~05s
|++++++++ | 16% ~05s
|+++++++++ | 17% ~05s
|++++++++++ | 18% ~05s
|++++++++++ | 19% ~05s
|+++++++++++ | 20% ~05s
|+++++++++++ | 21% ~05s
|++++++++++++ | 22% ~04s
|++++++++++++ | 23% ~04s
|+++++++++++++ | 24% ~04s
|+++++++++++++ | 26% ~04s
|++++++++++++++ | 27% ~04s
|++++++++++++++ | 28% ~04s
|+++++++++++++++ | 29% ~04s
|+++++++++++++++ | 30% ~04s
|++++++++++++++++ | 31% ~04s
|++++++++++++++++ | 32% ~04s
|+++++++++++++++++ | 33% ~04s
|++++++++++++++++++ | 34% ~04s
|++++++++++++++++++ | 35% ~04s
|+++++++++++++++++++ | 36% ~04s
|+++++++++++++++++++ | 37% ~04s
|++++++++++++++++++++ | 38% ~04s
|++++++++++++++++++++ | 39% ~04s
|+++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~03s
|++++++++++++++++++++++ | 43% ~03s
|++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|+++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|+++++++++++++++++++++++++ | 50% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|+++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~03s
|++++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|+++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|+++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|+++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06s
Calculating cluster RG3
| | 0 % ~calculating
|+ | 1 % ~04s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 4 % ~04s
|+++ | 5 % ~04s
|++++ | 6 % ~04s
|++++ | 7 % ~04s
|+++++ | 8 % ~04s
|+++++ | 9 % ~04s
|++++++ | 10% ~04s
|++++++ | 11% ~04s
|+++++++ | 12% ~04s
|+++++++ | 13% ~04s
|++++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|+++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|++++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 26% ~03s
|++++++++++++++ | 27% ~03s
|++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~03s
|+++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|+++++++++++++++++ | 34% ~03s
|++++++++++++++++++ | 35% ~03s
|++++++++++++++++++ | 36% ~03s
|+++++++++++++++++++ | 37% ~03s
|+++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 39% ~03s
|++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|+++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|++++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s
Calculating cluster RG4
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~03s
|++ | 3 % ~02s
|+++ | 5 % ~02s
|+++ | 6 % ~02s
|++++ | 7 % ~02s
|++++ | 8 % ~02s
|+++++ | 9 % ~02s
|++++++ | 10% ~02s
|++++++ | 11% ~02s
|+++++++ | 12% ~02s
|+++++++ | 14% ~02s
|++++++++ | 15% ~02s
|++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|++++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|+++++++++++ | 20% ~02s
|+++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 28% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s
Calculating cluster epi
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 4 % ~02s
|+++ | 5 % ~02s
|++++ | 7 % ~02s
|++++ | 8 % ~02s
|+++++ | 9 % ~02s
|+++++ | 10% ~02s
|++++++ | 11% ~02s
|+++++++ | 12% ~02s
|+++++++ | 13% ~02s
|++++++++ | 14% ~02s
|++++++++ | 15% ~02s
|+++++++++ | 16% ~02s
|+++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|+++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 29% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s
Calculating cluster RG5
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~03s
|++ | 3 % ~03s
|+++ | 4 % ~03s
|+++ | 5 % ~03s
|++++ | 7 % ~03s
|++++ | 8 % ~03s
|+++++ | 9 % ~03s
|+++++ | 10% ~03s
|++++++ | 11% ~03s
|+++++++ | 12% ~03s
|+++++++ | 13% ~03s
|++++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|+++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 29% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|+++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s
Calculating cluster opc
| | 0 % ~calculating
|+ | 1 % ~05s
|++ | 2 % ~05s
|++ | 3 % ~05s
|+++ | 4 % ~05s
|+++ | 5 % ~05s
|++++ | 6 % ~05s
|++++ | 7 % ~05s
|+++++ | 8 % ~05s
|+++++ | 9 % ~04s
|++++++ | 10% ~04s
|++++++ | 11% ~04s
|+++++++ | 12% ~04s
|+++++++ | 13% ~04s
|++++++++ | 14% ~04s
|++++++++ | 15% ~04s
|+++++++++ | 16% ~04s
|+++++++++ | 17% ~04s
|++++++++++ | 18% ~04s
|++++++++++ | 19% ~04s
|+++++++++++ | 20% ~04s
|+++++++++++ | 21% ~04s
|++++++++++++ | 22% ~04s
|++++++++++++ | 23% ~04s
|+++++++++++++ | 24% ~04s
|+++++++++++++ | 26% ~04s
|++++++++++++++ | 27% ~04s
|++++++++++++++ | 28% ~04s
|+++++++++++++++ | 29% ~04s
|+++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|+++++++++++++++++ | 34% ~03s
|++++++++++++++++++ | 35% ~03s
|++++++++++++++++++ | 36% ~03s
|+++++++++++++++++++ | 37% ~03s
|+++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 39% ~03s
|++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~03s
|+++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|+++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|+++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|+++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|++++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s
top3 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=3, wt = avg_log2FC)
DoHeatmap(RG.sub, features = top3$gene, size=5, angle =90, group.bar.height = 0.02)
Warning in DoHeatmap(RG.sub, features = top3$gene, size = 5, angle = 90, :
The following features were omitted as they were not found in the scale.data slot for the RNA assay: RPS13, RPL21, RPL34
marker.top3 <- unique(top3$gene)
DotPlot(RG.sub, features = marker.top3) + RotatedAxis()
top.10 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=10, wt = avg_log2FC)
The markers are all actually lower even though it says a positive LFC for RG1 (merged with 2)
Idents(RG.sub) <- "Level3"
RG1.markers.1 <- FindMarkers(RG.sub, ident.1 = "RG1", ident.2 = c("RG2","RG-OPC"))
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 4 % ~02s
|+++ | 6 % ~02s
|++++ | 7 % ~02s
|++++ | 8 % ~02s
|+++++ | 9 % ~04s
|+++++ | 10% ~03s
|++++++ | 11% ~03s
|+++++++ | 12% ~03s
|+++++++ | 13% ~03s
|++++++++ | 14% ~03s
|++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|+++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|++++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|+++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s
RG1.markers.2 <- FindMarkers(RG.sub, ident.1 = "RG1", ident.2 = c("RG2","RG3-NPC","RG4" ))
| | 0 % ~calculating
|+ | 1 % ~07s
|++ | 2 % ~07s
|++ | 3 % ~07s
|+++ | 4 % ~07s
|+++ | 5 % ~07s
|++++ | 6 % ~07s
|++++ | 7 % ~07s
|+++++ | 8 % ~06s
|+++++ | 9 % ~06s
|++++++ | 10% ~06s
|++++++ | 11% ~06s
|+++++++ | 12% ~06s
|+++++++ | 13% ~06s
|++++++++ | 14% ~06s
|++++++++ | 15% ~06s
|+++++++++ | 16% ~06s
|+++++++++ | 17% ~06s
|++++++++++ | 18% ~06s
|++++++++++ | 19% ~06s
|+++++++++++ | 20% ~06s
|+++++++++++ | 21% ~06s
|++++++++++++ | 22% ~06s
|++++++++++++ | 23% ~05s
|+++++++++++++ | 24% ~05s
|+++++++++++++ | 25% ~05s
|++++++++++++++ | 26% ~05s
|++++++++++++++ | 27% ~05s
|+++++++++++++++ | 28% ~05s
|+++++++++++++++ | 29% ~05s
|++++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~05s
|+++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 35% ~05s
|+++++++++++++++++++ | 36% ~05s
|+++++++++++++++++++ | 37% ~04s
|++++++++++++++++++++ | 38% ~04s
|++++++++++++++++++++ | 39% ~04s
|+++++++++++++++++++++ | 40% ~04s
|+++++++++++++++++++++ | 41% ~04s
|++++++++++++++++++++++ | 42% ~04s
|++++++++++++++++++++++ | 43% ~04s
|+++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|++++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~03s
|+++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|+++++++++++++++++++++++++++++ | 58% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|+++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07s
RG1.markers.3 <- FindMarkers(RG.sub, ident.1 = "RG1", ident.2 = c("RG2","RG3-NPC","RG4","RG5","RG6-epi","RG-OPC"))
| | 0 % ~calculating
|+ | 1 % ~07s
|++ | 2 % ~07s
|++ | 3 % ~06s
|+++ | 4 % ~06s
|+++ | 5 % ~06s
|++++ | 6 % ~06s
|++++ | 7 % ~06s
|+++++ | 9 % ~06s
|+++++ | 10% ~06s
|++++++ | 11% ~06s
|++++++ | 12% ~06s
|+++++++ | 13% ~06s
|+++++++ | 14% ~06s
|++++++++ | 15% ~06s
|++++++++ | 16% ~06s
|+++++++++ | 17% ~06s
|++++++++++ | 18% ~06s
|++++++++++ | 19% ~05s
|+++++++++++ | 20% ~05s
|+++++++++++ | 21% ~05s
|++++++++++++ | 22% ~05s
|++++++++++++ | 23% ~05s
|+++++++++++++ | 24% ~05s
|+++++++++++++ | 26% ~05s
|++++++++++++++ | 27% ~05s
|++++++++++++++ | 28% ~05s
|+++++++++++++++ | 29% ~05s
|+++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~05s
|++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++ | 34% ~04s
|++++++++++++++++++ | 35% ~04s
|+++++++++++++++++++ | 36% ~04s
|+++++++++++++++++++ | 37% ~04s
|++++++++++++++++++++ | 38% ~04s
|++++++++++++++++++++ | 39% ~04s
|+++++++++++++++++++++ | 40% ~04s
|+++++++++++++++++++++ | 41% ~04s
|++++++++++++++++++++++ | 43% ~04s
|++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|+++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~03s
|+++++++++++++++++++++++++ | 50% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|+++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~03s
|++++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|+++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|+++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|+++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07s
table(RG.sub$Level3)
Astro1 Neurons1 RG1 Astro2 Neurons2 Astro3
0 0 817 0 0 0
Astro4 Neurons3 RG2 Neurons4 Astro5 Neurons5
0 0 341 0 0 0
RG3-NPC DANeurons1 Neurons6 Mix RG4 Endothelial-NPC
304 0 0 0 211 0
Neurons8-inh-max DANeurons2 DANeurons3 RG5 RG6-epi RG7
0 0 0 99 57 45
RG-OPC
39
I’ll have a look at the predictions and marker list
# predictions
t.lables <- as.data.frame(table(RG.sub$Level3, RG.sub$Bha.mid.stri.pred))
t.lables <- as.data.frame(table(RG.sub$Level3, RG.sub$AIW60.pred))
t.lables <- as.data.frame(table(RG.sub$Level3, RG.sub$AIW120.pred))
t.lables <- as.data.frame(table(RG.sub$Level3, RG.sub$MBOAIW.pred)) # same as AIW120 but predictions are slighlty different, mostly the same
t.lables <- as.data.frame(table(RG.sub$Level3, RG.sub$MBOAST23.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
There are not really RG in mature brains
Do some more predictions with the developing Brain
Now the developing cortex data
Have a look at the potential markers for RG1 and RG2
markers <- c("TMSB4X", "TFPI2","RBP1", "LRRC75A", "RPS12", "RPL34", "RPS13")
DotPlot(RG.sub, features= markers, group.by = 'Level3') +RotatedAxis()
DotPlot(RG.sub, features= markers, group.by = 'Level4') +RotatedAxis()
All the RG1 markers are high in RG-OPC, but that small cluster is predicted differently from RG1
Clustermarkers <- FindAllMarkers(RG.sub, test.use = "MAST", only.pos = TRUE)
Calculating cluster RG1
Completed [==>-------------------------------------------------------------------] 5% with 0 failures
Completed [===>------------------------------------------------------------------] 5% with 0 failures
Completed [===>------------------------------------------------------------------] 6% with 0 failures
Completed [====>-----------------------------------------------------------------] 7% with 0 failures
Completed [====>-----------------------------------------------------------------] 8% with 0 failures
Completed [=====>----------------------------------------------------------------] 8% with 0 failures
Completed [=====>----------------------------------------------------------------] 9% with 0 failures
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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
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Done!
Calculating cluster RG2
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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
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Done!
Calculating cluster RG3-NPC
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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
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Done!
Calculating cluster RG4
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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
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Done!
Calculating cluster RG5
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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
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Done!
Calculating cluster RG6-epi
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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
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Done!
Calculating cluster RG7
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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
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Done!
Calculating cluster RG-OPC
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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
Completed [>---------------------------------------------------------------------] 2% with 0 failures
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Done!
Check out the down regulated pathways
Er <- enrichr(genes, databases = db)
Uploading data to Enrichr... Done.
Querying GO_Cellular_Component_2015... Done.
Querying GO_Biological_Process_2015... Done.
Querying Human_Gene_Atlas... Done.
Querying KEGG_2013... Done.
Parsing results... Done.
print(plotEnrich(Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
t.GOcell <- Er[[1]] %>% select(Term, Genes, Combined.Score)
print(t.GOcell)
t.GObio <- Er[[2]] %>% select(Term, Genes, Combined.Score)
print(t.GObio)
t.CellMarker <- Er[[3]] %>% select(Term, Genes, Combined.Score)
print(t.CellMarker)
t.Azi <- Er[[4]] %>% select(Term, Genes, Combined.Score)
print(t.Azi)
# no
Check markers from Pollen … Kriegstein
# RG markers were up in one group of RG and down another
# SLC1A3, PAX6, SOX2, PDGFD, GLI3 up in own group, the other has usp STMN2 and NEUROD6
# rg.con <- c("VIM","HES")
rg <- c("SLC1A3", "PAX6", "SOX2", "PDGFD", "GLI3", "STMN2", "NEUROD6", "VIM", "HES1")
DotPlot(RG.sub, features= rg, group.by = 'Level3') +RotatedAxis()
# RG4 matches the first pop SLCI1A3, PAX6 SOX2
# progenitor markers canonical and novel
# EOMES = TBR2
pro <- c("TBR2", "ELAVL4", "NEUROG1", "NEUROD1", "NEUROD4", "PPP1R17", "PENK")
DotPlot(RG.sub, features= pro, group.by = 'Level3') +RotatedAxis()
Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
The following requested variables were not found: TBR2
# RP7 has some progen markers NEUROD4
# RG-OPC expresses PENK progenitor markers
# CRYAB is a novel G1 marker
# ventral RG markers
rgv <- c("CRYAB", "PDGFD", "TAGLN2", "FBXO32", "PALLD")
# out svz
osvz <- c("HOPX", "PTPRZ1", "TNC", "FAM107A", "MOXD1")
DotPlot(RG.sub, features= rgv, group.by = 'Level3') +RotatedAxis()
DotPlot(RG.sub, features= osvz, group.by = 'Level3') +RotatedAxis()
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RG4,5,6 classic RG markers and indicated as outer SVZ RG RG7 has intermediate progenitor markers RG2 - deviding cells use CRYAB as a marker RG1 is likely the ventral RG - mark as SOX2- RG-OPC has his VIM and also Ventral - TAGLN2
Not sure which marker is good for RG3
rg3 <- c("NEAT1", "POLR2J3.q", "ZMAT1", "DDX17", "PNISR", "LUC7L3",
"DST", "MACF1", "ARGUL1", "NKTR", "WSB1", "SFPQ")
DotPlot(RG.sub, features= rg3, group.by = 'Level3') +RotatedAxis()
Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
The following requested variables were not found: POLR2J3.q, ARGUL1
Still need markers for 5 and 6 Check the list and also distinguish from each other
FeaturePlot(seu.q, features = c("CP", "ECEL1", "EMX2", "VIM", "SOX2","HES1","PAX6"))
DimPlot(seu.q, label = TRUE)
# see each of the markers
rg.sub.markers <- c("SOX2","CRYAB","DDX17","RTPRZ1","CLU","CP","MALAT1","NEUROD4","PENK")
DimPlot(seu.q, label = TRUE)
FeaturePlot(seu.q, features = rg.sub.markers)
Warning in FetchData.Seurat(object = object, vars = c(dims, "ident", features), :
The following requested variables were not found: RTPRZ1
FeaturePlot(RG.sub, features = "SOX2", split.by = 'Level3')
# RG-OPC is SOX2 negative
# RG epi and RG7 very low and few
# RG1 mostly negative but a few very positive cells
FeaturePlot(RG.sub, features = "HES1", split.by = 'Level3')
# a little all over almost all cells high in RG4
FeaturePlot(RG.sub, features = "CRYAB", split.by = 'Level3')
Time to add on the final subgroup annotations - double check what makes sense It might be easier to split the Neurons and Radial glia into subgroups and then compare between those subgroups. RG it’s harder different groups overlap
It seems like RG1,2,4 are more similar
RG4 and RG6 are also very similar RG4 is the most ‘true’ RG
RG4 and 2 overlap a lot, also RG6 overlaps with 4 but not with 6
All have VIM RG1 is negative low other markers HES1, SOX2, PAX6 RG-OPC is negative in PAX6 RG5 and 6 have high upregulation of mitochondrial energy production transcripts
MALAT1 high in 5,6,7 and none in the other groups RG7 more and RG-OPC have some intermediate progenitor markers
5 and 6 are not having tones of differentially expressed genes from eachother, but those genes are overlapping with the other groups
I might merge 2 and 4 previously tested 2vs4 and nothing is up in 2 compared to 4
# neuroD1 very high expression in RG7 but maybe not in all the cells
# test many markers - I've type in and removed for speed
# "SPARCL1"
# DLK1 distinguishes 5 vs 6 but it expressed in most other than 6
# save thing for PTN and ESM1
# up in 6 : MGP actually low in 6 but consistant, not in 5
# FHIT no expressed in all 6 and low everywhere
FeaturePlot(RG.sub, features = c("ID1"), split.by = 'Level3')
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